EOG-based method and apparatus for asynchronous character input

ABSTRACT

The present invention discloses an EOG-based method and apparatus for asynchronous character input, said method comprising the following steps: displaying a virtual keyboard having a total of N keys on a display, the virtual keyboard flickering in rounds, wherein in each round, all N keys randomly flicker once; when spelling, a user blinks after the flickering of a target key, so as to acquire electro-oculogram signals of the user in real-time; capturing electro-oculogram data within a time period following the flickering of a key from the acquired electro-oculogram signals, and using said electro-oculogram data as an original feature vector of the flickering key; N original feature vectors being generated in each cycle, an electro-oculogram identification method is called to identify the N original feature vectors, and outputting a certain result from 0 to N, wherein 0 indicates that a condition for character input is not met, and 1 to N correspond to the N keys on the virtual keyboard. The present invention ensures that blinking by a user may be detected normally, while at the same time effectively eliminating misjudgments of non-blinking signals, thus increasing the accuracy rate of character input.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is the U.S. National Stage of International Application No.PCT/CN2017/110102 filed Nov. 9, 2017, which was published in Chineseunder PCT Article 21(2), and which in turn claims the benefit of ChinaPatent Application No. 201611118744.9 filed Dec. 8, 2016.

FIELD OF THE INVENTION

The present invention belongs to the technical field of character inputrelying on human biological signals, and particularly relates to anEOG-based method and apparatus for asynchronous character input.

BACKGROUND OF THE INVENTION

In order to enable disabled people with motor dysfunction to interactwith the outside world and improve their quality of life and self-careability, a new type of human-machine interface that uses humanbioelectrical signals to communicate with computers or external deviceshas become the frontiers and hot spots of research in recent years. Thehuman bioelectrical signals mainly include electroencephalogram (EEG),electromyography (EMG), electrocardiogram (ECG), and electrooculogram(EOG) signals. The human-machine interface based on the bioelectricalsignals can achieve interaction with the outside world without anysignificant movement of a user, which is of great significance fordisabled people with severe movement disorders (such as amyotrophiclateral sclerosis ALS, brainstem stroke, spinal cord injury SCI, etc.).

In the above bioelectrical signals, the ECG signal is difficult to becontrolled by humans, the EMG signal requires the user to have muscletissue that is both mobile and suitable for use as a control signal, andthe EEG signal is complex and weak and more difficult to implement thehigh-performance human-machine interface. Therefore, a considerable partof the human-machine interface involved in the prior art uses the EOGsignal. The EOG signal is a bioelectrical signal generated by movementsof the eyeball such as horizontal movement, vertical movement, rotationor blinking.

The Chinese invention patent application with the publication numberCN1601445A disclosed “A Multi-Function Human BiologicalElectro-Oculogram Switch Control Device” on Mar. 30, 2005. This solutionused the EOG signals corresponding to a subject's consecutive 3, 4 and 5blinks as a control command for switching external devices. The controlcommand of this method is single and cannot meet the requirements ofcomplicated human-machine interaction tasks.

The Chinese invention patent application with the publication numberCN102129307A disclosed “A Computer Input Control Method Based onElectro-oculogram Signals” on Jul. 20, 2011. In this solution, the rowsand columns in the virtual keyboard were highlighted in a cyclic manner,and the electro-oculogram signal was acquired and identified by theelectro-oculogram acquisition module. When the electro-oculogram signalwas identified, the currently highlighted row and column was determinedas the row and column where the target character was. Theelectro-oculogram identification strategy adopted by this solution wasas follows: when the amplitude of the signal was detected to be greaterthan 120 μV for consecutive 30 ms, the signal was judged to be a blink.Although the method disclosed in this patent does enable complexcomputer input control, it at least has the following disadvantages:Firstly, in one example disclosed in this patent, the keyboard areaconsisting of 7 rows and 8 columns was cyclically highlighted in theform of rows and columns, with 1 s for each highlighting. Therefore,theoretically one round of cyclic highlighting cost 15 s. Assuming thatevery blinking could be successfully detected, the average time requiredto enter a character was 7.5 s (rows and columns did not need to fullyflicker). If a blinking was unsuccessfully detected, the time wouldincrease by at least 7-8 s, so the character input was slower. Secondly,the disclosed method did not control the misjudgments very well. Whenthe device was started but the user did not want to input characterstemporarily, it might also cause erroneous input due to involuntaryblinking. Finally, the blinking detection strategy of the disclosedmethod was too simple, only judging the signal within consecutive 30 msaccording to the threshold. This judgment condition was too broad,because other movements of the eyeball such as horizontal glance,squint, rotation and jump were also very easy to generate anelectro-oculogram wave of more than 120 μV and misjudged as blinking,which in turn caused erroneous input. In fact, however, theseelectro-oculogram activities are significantly different from blinkingin the waveform.

Therefore, it is important to provide a method and apparatus foraccurately inputting characters based on an electro-oculogram signal.

SUMMARY OF THE INVENTION

The main object of the present invention is to overcome the defects andshortcomings of the prior art and provide an EOG-based method forasynchronous character input, which ensures that blinking by a user maybe detected normally, while at the same time effectively eliminatingmisjudgments of non-blinking signals, thus increasing the accuracy rateof character input.

Another object of the present invention is to provide an EOG-basedapparatus for asynchronous character input, which can ensure thatblinking by a user may be detected normally, while at the same timeeffectively eliminating misjudgments of non-blinking signals, thusincreasing the accuracy rate of character input.

The objects of the present invention are achieved by the followingtechnical solution: An EOG-based method for asynchronous character inputis provided, comprising the following steps:

displaying a virtual keyboard assumed to have a total of N keys on adisplay, the virtual keyboard flickering in rounds, wherein in eachround, all N keys randomly flicker once; when spelling, a user blinksafter the flickering of a target key, so that an electro-oculogramacquisition module acquires electro-oculogram signals of the user inreal-time; capturing electro-oculogram data within a time periodfollowing the flickering of a key from the acquired electro-oculogramsignals, and using said electro-oculogram data as an original featurevector of the flickering key; N original feature vectors being generatedin each cycle, an electro-oculogram identification method is called toidentify the N acquired original feature vectors, and anelectro-oculogram identification algorithm will output a certain resultfrom 0 to N, wherein 0 indicates that a condition for character input isnot met, and 1 to N correspond to the N keys on the virtual keyboard.

Specific steps are as follows:

S1: the virtual keyboard flickers in rounds, wherein in each round, allN keys randomly flicker once; when spelling, a user blinks after theflickering of a target key, so that an electro-oculogram acquisitionmodule acquires electro-oculogram signals of the user in real-time;electro-oculogram data are captured within a time period of 100-500 msfollowing the flickering of a key from the acquired electro-oculogramsignals, and used as an original feature of this key in this round offlickering;

S2: the captured original feature data are pre-processed in sequenceincluding removing baseline drift, removing 50 Hz power frequencyinterference, and 0.1-30 Hz bandpass filtering;

S3: a first-order difference of the electro-oculogram datum x after thepre-processing in the step S2 is found, which is performed specificallyby the following method:d _(i) =x _(i+1) −x _(i)where i denotes an i-th sampling point and d denotes a differentialsignal;

S4: N features of D=[d₁, d₂, . . . , d_(N)] after the differencecorrespond to N key flickerings in one round; SVM classification andwaveform detection are performed to obtain N SVM classification resultsof S=[s₁, s₂, . . . , s_(i), . . . , s_(N)] and N waveform detectionresults of W=[w₁, w₂, . . . , w_(i), . . . , w_(N)], wherein only thefirst largest M scores are retained for the SVM classification result Swith the rest set to zero; the SVM classification result S and thewaveform detection result W are multiplied to obtain R^(t), where tdenotes the t-th round:R ^(t=[) r ₁ ^(t) ,r ₂ ^(t) , . . . ,r _(i) ^(t) . . . ,r _(N) ^(t)]=[s₁·w ₁ ,s ₂ ·w ₂ , . . . ,s _(i) ·w _(i) , . . . ,s _(N) ·w _(N)]and R^(t) is an N-dimensional row vector containing M non-zero values;

S5: R^(t) is traversed, and if r_(i) ^(t)>0 does not exist, there is notarget output in the current round, and the process returns to the stepS1 to continue the detection; if r_(i) ^(t)>0 exists, the key thatsatisfies r_(i) ^(t)>0 is regarded as the candidate target of thecurrent round for further judgment; and

S6: in three consecutive detections (t-th, t-1-th and t-2-thdetections), if no key is detected to be a candidate target twice, thenthere is no target output in the current round, the output result is 0,and the process returns to the step S1 to continue the detection;otherwise, a key corresponding to the maximum r_(i) ^(t)+r_(i)^(t-1)+r_(i) ^(t-3) is determined as the target, and its correspondingoperation is performed according to the key, and then the processproceeds to the step S1 to continue the detection.

Preferably, the virtual keyboard has a total of 40 keys in 4 rows and 10columns.

Specifically, all the keys flicker in a “conditional random” manner thatis specifically as follows: with a round taking 1.2 s, each of the 40keys will flicker once in a round for a duration of 100 ms at aninterval of 30 ms between two adjacent flickering keys.

Further, the “conditional random” flickering manner means that theflickering order of 40 keys is random in each round, but the followingtwo conditions need to be met:

(1-1) any key flickers at an interval of not being less than 600 ms intwo consecutive rounds; and

(1-2) when the flickering order of the keys in one round is determined,it is necessary to determine the flickering order of the keys in theprevious two rounds, that is, trying to ensure that characters appearedaround any character in the previous two rounds do not again appeararound this character in the current round.

Preferably, the waveform detection in the step S4 comprises thefollowing steps:

The following three conditions are detected for a differential signalD_(i): a. a trough appears 40-140 ms after the appearance of a peak; b.the peak/trough corresponds to the maximum/minimum point of the entiresegment of the signal; and c. the sum of the energy of the signal fromthe peak to the trough is greater than a preset threshold P.

When all the three conditions are fulfilled in the detection, thewaveform of the key i corresponding to the differential signal D_(i)passes the detection; with w₁ taking a value of 0 or 1, w_(i)=0indicates that the waveform of the feature d_(i) does not pass thedetection, and w₁=1 indicates that the waveform of the feature d_(i)passes the detection.

Preferably, the SVM classifier used in the step S4 needs to be trainedin advance, and the training process comprises the following steps:

(4-1) The electro-oculogram data are acquired from several trials,wherein each of the trials has a specified target key and is carried outfor several rounds, and the N keys randomly flicker once in each round,requiring that a user blinks only when the target key flickers, so thateach key generates one feature vector in each round in each trial, andall the feature vectors contain positive and negative samples requiredto train the SVM classifier;

(4-2) among all the acquired feature vectors, the features correspondingto the target key are regarded as positive sample features and given alabel 1, and the features corresponding to the non-target key areregarded as negative sample features and given a label−1; and

(4-3) the SVM classifier is trained with these labeled positive andnegative samples.

Preferably, when the virtual keyboard has 40 keys, M takes a value of 5in the step S4.

An EOG-based apparatus for asynchronous character is provided,comprising an electrode, an electro-oculogram acquisition module, acomputer host and a display; the electrode is connected to theelectro-oculogram acquisition module through a self-contained connectingwire, an electro-oculogram signal obtained by the electro-oculogramacquisition module is transmitted to the computer host through a commonserial port, and the computer host is connected to the display through acommon VGA cable.

Preferably, the electrode is a common silver chloride electrode.

Preferably, the electro-oculogram acquisition module comprises a signalamplification module, a signal filtering module, an A/D conversionmodule, and a serial communication module, wherein:

(1) the signal amplification module is based on an INA128 chip, having acommon mode rejection ratio of 120 db or more and an amplificationfactor of 1000 times or more;

(2) the signal filtering module is based on an OPA4227 chip, comprisinga second-order Butterworth high-pass filter with a cutoff frequency of 3Hz, a fourth-order Butterworth low-pass filter with a cutoff frequencyof 25 Hz, and a Butterworth 50 Hz trap filter.

(3) the A/D conversion module is based on an AD7606 chip, having aconversion accuracy of 16 bits; and

(4) the serial communication module adopts a serial communication modulethat comes with STM32.

Compared with the prior art, the present invention has the followingadvantages and benefits:

(1) In the blinking detection, the present invention combines the twomethods of waveform matching and SVM classification, and substantiallyeliminates misjudgments of non-blinking signals while ensuring thatblinking is normally detected.

(2) The present invention strictly uses the flickering timing of thekeys as the moment when the user blinks. On the one hand, the accuracyof the blinking detection is increased; and on the other hand, aphysiologically indistinct blinking motion can be corresponding to 40different key operations. In turn, the EOG virtual keyboard of up to 40different keys disclosed in the present invention is realized, and thecharacter input is faster.

(3) The “conditional random flicker” extracted by the present inventioncan well solve the misjudgment of non-target keys with close timing.

(4) The present invention adopts a mechanism that being detected 2 timesin 3 rounds is considered as target input, which not only helps toreduce the misjudgments caused by the non-autonomous blink, but alsoreduces the misjudgments of the autonomous blinking as other keys.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view showing the arrangement of electrodes in thisexample;

FIG. 2 is a block diagram of an electro-oculogram acquisition module inthis example;

FIG. 3 is a flow chart of the EOG-based method for asynchronouscharacter input in this example; and

FIG. 4 is a schematic diagram of a virtual keyboard in this example.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The present invention will be further described in detail below withreference to examples and drawings, but the embodiments of the presentinvention are not limited thereto.

Referring to FIGS. 1 and 2, an EOG-based apparatus for asynchronouscharacter input of this example is provided, comprising an electrode, anelectro-oculogram acquisition module, a computer host and a display. Theelectrode is connected to the electro-oculogram acquisition modulethrough a self-contained connecting wire, an electro-oculogram signalobtained by the electro-oculogram acquisition module is transmitted tothe computer host through a common serial port, and the computer host isconnected to the display through a common VGA cable.

The electrode is a common silver chloride electrode. As shown in FIG. 1,this example includes three electrodes (GND: ground; E:electrooculogram; REF: reference) for acquiring the electro-oculogramsignals of a subject.

The electro-oculogram acquisition module uses a sampling rate of 250 Hzwhen acquiring electrooculogram. As shown in FIG. 2, theelectro-oculogram acquisition module comprises four sub-modules: signalamplification, signal filtering, A/D conversion, and serialcommunication. The characteristics of each sub-module are as follows:

(1) The signal amplification module is mainly based on the INA128 chipdesign, having a common mode rejection ratio of 120 db or more and anamplification factor of 1000 times or more;

(2) the signal filtering module is mainly based on the OPA4227 chipdesign, comprising a second-order Butterworth high-pass filter with acutoff frequency of 3 Hz, a fourth-order Butterworth low-pass filterwith a cutoff frequency of 25 Hz, and a Butterworth 50 Hz trap filter;

(3) the A/D conversion module is mainly based on the AD7606 chip design,having a conversion accuracy of 16 bits; and

(4) the serial communication module is implemented mainly based on aserial communication module that comes with STM32.

In this example, the computer host is a normal PC (desktop or laptop),and the operating system is Windows XP or Windows 7. The display is anormal liquid crystal display.

The EOG-based method for asynchronous character input based on the aboveapparatus of this example, as shown in FIG. 3, comprises the followingsteps:

S1: Displaying a virtual keyboard having a total of 40 keys in 4 rowsand 10 columns on a display, as shown in FIG. 4.

All the keys of the virtual keyboard flicker in a “conditional random”manner. The specific flickering method is 1.2 s for one round, and eachof the 40 keys will flicker once in one round; each key flickers for aduration of 100 ms, and the interval between two adjacent flickeringkeys is 30 MS.

The “conditional random” flickering manner means that the flickeringorder of 40 keys is random in each round, but the following twoconditions need to be met:

(1) Any key flickers at an interval of not being less than 600 ms in twoconsecutive rounds.

(2) When the flickering order of the keys in one round is determined, itis necessary to consider the flickering order of the keys in theprevious two rounds, that is, trying to ensure that characters appearedaround any character in the previous two rounds do not again appeararound this character. Assuming that the three keys that appear beforeand after the key 10 in the i-th round form an set A{1, 2, 3, 5, 12, 6},and the three keys that appear before and after the key 10 in the i+1-thround form a set B{4, 14, 8, 30, 36, 11}, when the flickering order ofthe i+2-th round is determined, the three flickering keys before andafter the key 10 cannot belong to the set A or the set B.

S2: The virtual keyboard flickers in rounds in the manner of the stepS1, and when spelling, the user is required to blink following theflickering of the target key; the electro-oculogram data within 100-500ms following the flickering of a key are captured from theelectro-oculogram signal acquired by the electro-oculogram acquisitionmodule, and used as the original feature of the flickering key; witheach round corresponding to 40 original feature vectors, anelectro-oculogram identification algorithm is called to identify the 40acquired original feature vectors, and will output a certain result from0 to 40, wherein 0 indicates that a condition for character input is notmet, and 1 to 40 correspond to the 40 keys on the interface of the step2.

The detailed process of this step comprises the following steps:

S2-1: The electro-oculogram data within 100-500 ms following theflickering of each key is captured as an original feature.

Note that the time interval between the two flickering keys in the stepS1 is 30 ms, so the original features of the keys will have dataoverlap, but such overlap does not affect the implementation of thepresent invention.

S2: The acquired original feature data with a duration of 400 ms arepre-processed in sequence including removing baseline drift, removing 50Hz power frequency interference, and 0.1-30 Hz bandpass filtering.

S3: A first-order difference of the electro-oculogram datum x after thepre-processing in the step S2-2 is found, which is performedspecifically by the following method:d _(i) =x _(i+1) −x _(i)Where i denotes the i-th sampling point (the sampling frequency is 250Hz, and the feature of 400 ms contains a total of 100 sampling points),and d denotes the signal after the difference.

S2-4: Forty features of D=[d₁, d₂, . . . , d₄₀] after the differencecorrespond to 40 key flickerings in a round, and SVM classification andwaveform detection are performed to obtain 40 SVM classification resultsof S=[s₁, s₂, . . . , s_(i), . . . , s₄₀] and 40 waveform detectionresults of W=[w₁, w₂, . . . , w_(i), . . . , w₄₀], wherein only thefirst largest 5 scores are retained for S with the rest set to zero. TheSVM classification result S and the waveform detection result W aremultiplied to obtain R^(t).R ^(t)=[r ₁ ^(t) ,r ₂ ^(t) , . . . ,r ₄₀ ^(t)]=[s₁ ·w ₁ ,s ₂ ·w ₂ , . .. ,s ₄₀ ·w _(′].)

S2-5: R^(t) is traversed, and if r_(i) ^(t)>0 does not exist, there isno target output in the current round, and the process returns to thestep S2-1 to continue the detection; if r_(i) ^(t)>0 exists, the keythat satisfies r_(i) ^(t)>0 is regarded as the candidate target of thecurrent round for further judgment.

S2-6: In three consecutive detections (t-th, t-1-th and t-2-thdetections), if no key is detected to be a candidate target twice, thenthere is no target output in the current round, the output result is 0,and the process returns to the step S2-1 to continue the detection;otherwise, the key corresponding to the maximum r_(i) ^(t)+r_(i)^(t-1)+r_(i) ^(r-2) is determined as the target, and its correspondingoperation is performed, such as outputting the corresponding character,deleting the character, case switching, and keyboard locking, and thenthe process proceeds to the step S2-1 to continue the detection.

For example, if the keys 1, 2, 3 are candidates in the t-1-th round, andthe keys 2, 3, 4 are candidates in the t-th round, the keys 2, 3 satisfythe condition and are then calculated; since there must be no key 2 or 3as the candidate in the t-2-th round (otherwise, 2 or 3 should be outputin the t-1-th round), the sum of the r values of the keys 2, 3 in thet-2-th, t-1-th and t-th rounds is calculated so as to select the largestas the target. Note that the r value of the t-2-th round is actually 0,because there is no 2 or 3 in the candidates of the t-2-th round.

The waveform detection in the step S2-4 refers to the detection of thefollowing three conditions for the differential signal D_(i): (1) Atrough appears 40-140 ms after the appearance of a peak; (2) thepeak/trough does correspond to the maximum/minimum point of the entiresegment of the signal; and (3) the sum of the energy of the signal fromthe peak to the trough is greater than a preset threshold P. When allthe three conditions are fulfilled in the detection, the waveform of thekey i corresponding to the differential signal D_(i) passes thedetection. With w_(i) taking a value of 0 or 1, w_(i)=0 indicates thatthe waveform of the feature d_(i) does not pass the detection, andw_(i)=1 indicates that the waveform of the feature d_(i) passes thedetection.

The SVM classifier used in the step S2-4 needs to be trained in advance,and the training process comprises the following steps: (1) Theelectro-oculogram data are acquired from 20 trials according to thesteps S2-1, S2-2 and S2-3, wherein each of the trials has a specifiedtarget key and includes 10 rounds, and the 40 keys randomly flicker oncein each round, requiring that a user blinks only when the target keyflickers, so that the acquired data contain positive and negativesamples required to train the SVM classifier; (2) in all the acquired8000 (20 trials×10 rounds×40 keys) feature vectors, the featurescorresponding to the target key are regarded as positive sample features(200 in total) and given a label 1, and the features corresponding tothe non-target key are regarded as negative sample features (7800 intotal) and given a label −1; and (3) the SVM classifier is trained withthese labeled positive and negative samples.

Because the blinking waveform is relatively stable, and thediscrimination between the two types (a blinking signal and anon-blinking signal) is relatively large, a trained SVM classifier hasbeen tested and can be used by different users for a long time; if it isnot effective for some users, a specific SVM classifier can be retrainedin a targeted manner.

The above-described examples are preferred embodiments of the presentinvention, but the embodiments of the present invention are not limitedthereto, and any other alterations, modifications, substitutions,combinations and simplifications should be equivalent replacements andincluded in the scope of protection of the present invention.

The invention claimed is:
 1. An EOG-based method for asynchronouscharacter input, characterized in that: the method comprises thefollowing steps: displaying a virtual keyboard having a total of N keyson a display, the virtual keyboard flickering in rounds, wherein in eachround, all N keys randomly flicker once; when spelling, a user blinksafter the flickering of a target key, so that an electro-oculogramacquisition module acquires electro-oculogram signals of the user inreal-time; capturing electro-oculogram data within a time periodfollowing the flickering of a key from the acquired electro-oculogramsignals, and using said electro-oculogram data as an original featurevector of the flickering key; N original feature vectors being generatedin each cycle, an electro-oculogram identification algorithm is calledto identify the N acquired original feature vectors, and theelectro-oculogram identification algorithm will output a certain resultfrom 0 to N, wherein 0 indicates that a condition for character input isnot met, and 1 to N correspond to the N keys on the virtual keyboard. 2.The EOG-based method for asynchronous character input according to claim1, characterized in that: the method comprises the following steps: S1:the virtual keyboard flickers in rounds, wherein in each round, all Nkeys randomly flicker once; when spelling, a user is required to blinkafter the flickering of a target key, so that the electro-oculogramacquisition module acquires electro-oculogram signals of the user inreal-time; electro-oculogram data are captured within a time period of100-500 ms following the flickering of a key from the acquiredelectro-oculogram signals, and used as the original feature vector ofthis key in this round of flickering; S2: the captured original featuredata are pre-processed in sequence including removing baseline drift,removing 50 Hz power frequency interference, and 0.1-30 Hz bandpassfiltering; S3: a first-order difference of the electro-oculogram datum xafter the pre-processing in the step S2 is found, which is performedspecifically by thed _(i) =x _(i+1) −x _(i) where i denotes an i-th sampling point and ddenotes a differential signal; S4: N features of D=[d₁, d₂, . . . ,d_(N)] after the difference correspond to N key flickerings in oneround; SVM classification and waveform detection are performed to obtainN SVM classification results of S=[s₁, s₂, . . . , s_(i), . . . , s_(N)]and N waveform detection results of W=[W₁, W₂, . . . , W_(i), . . . ,W_(N)], wherein only the first largest M scores are retained for the SVMclassification result S with the rest set to zero; the SVMclassification result S and the waveform detection result W aremultiplied to obtain R^(t), where t denotes the t-th round:R ^(t=[) r ₁ ^(t) ,r ₂ ^(t) , . . . ,r _(i) ^(t) . . . ,r _(N) ^(t)]=[s₁·w ₁ ,s ₂ ·w ₂ , . . . ,s _(i) ·w _(i) , . . . ,s _(N) ·w _(N)] andR^(t) is an N-dimensional row vector containing M non-zero values; S5:R^(t) is traversed, and if r_(i) ^(t)>0 does not exist, there is notarget output in the current round, and the process returns to the stepS1 to continue the detection; if r_(i) ^(t)>0 exists, the key thatsatisfies r_(i) ^(t)>0 is regarded as the candidate target of thecurrent round for further judgment; and S6: in three consecutivedetections (t-th, l-th and t-2-th detections), if no key is detected tobe a candidate target twice, then there is no target output in thecurrent round, the output result is 0, and the process returns to thestep S1 to continue the detection; otherwise, a key corresponding to themaximum r_(i) ^(t)+r_(i) ^(t-1)+r_(i) ^(t-3) is determined as thetarget, and its corresponding operation is performed according to thekey, and then the process proceeds to the step S1 to continue thedetection.
 3. The EOG-based method for asynchronous character inputaccording to claim 2, characterized in that: the virtual keyboard has atotal of 40 keys in 4 rows and 10 columns, and all the keys flicker in a“conditional random” manner that is specifically as follows: with around taking 1.2 s, each of the 40 keys will flicker once in a round fora duration of 100 ms at an interval of 30 ms between two adjacentflickering keys.
 4. The EOG-based method for asynchronous characterinput according to claim 3, characterized in that: the “conditionalrandom” flickering manner means that the flickering order of 40 keys israndom in each round, but the following two conditions need to be met:(1-1) any key flickers at an interval of not being less than 600 ms intwo consecutive rounds; and (1-2) when the flickering order of the keysin one round is determined, it is necessary to determine the flickeringorder of the keys in the previous two rounds, that is, trying to ensurethat characters appeared around any character in the previous two roundsdo not again appear around this character in the current round.
 5. TheEOG-based method for asynchronous character input according to claim 2,characterized in that: the waveform detection in the step S4 comprisesthe following steps: the following three conditions are detected for adifferential signal D_(i): a. a trough appears 40-140 ms after theappearance of a peak; b. the peak/trough corresponds to themaximum/minimum point of the entire segment of the signal; and c. thesum of the energy of the signal from the peak to the trough is greaterthan a preset threshold P; when all the three conditions are fulfilledin the detection, the waveform of the key i corresponding to thedifferential signal D_(i) passes the detection; with Iv, taking a valueof 0 or 1, w_(i)=0 indicates that the waveform of the feature d_(i) doesnot pass the detection, and w_(i)=1 indicates that the waveform of thefeature d_(i) passes the detection.
 6. The EOG-based method forasynchronous character input according to claim 2, characterized inthat: the SVM classifier used in the step S4 needs to be trained inadvance, and the training process comprises the following steps: (4-1)the electro-oculogram data are acquired from several trials, whereineach of the trials has a specified target key and is carried out forseveral rounds, and the N keys randomly flicker once in each round,requiring that a user blinks only when the target key flickers, so thateach key generates a feature vector in each round in each trial, and allthe feature vectors contain positive and negative samples required totrain the SVM classifier; (4-2) among all the acquired feature vectors,the features corresponding to the target key are regarded as positivesample features and given a label 1, and the features corresponding tothe non-target key are regarded asnegative sample features and given alabel −1; and (4-3) the SVM classifier is trained with these labeledpositive and negative samples.
 7. The EOG-based method for asynchronouscharacter input according to claim 2, characterized in that: when thevirtual keyboard has 40 keys, M takes a value of 5 in the step S4.
 8. Aninput apparatus characterized in that: this apparatus comprises anelectrode, an electro-oculogram acquisition module, a computer host anda display; the electrode is connected to the electro-oculogramacquisition module through a self-contained connecting wire, anelectro-oculogram signal obtained by the electro-oculogram acquisitionmodule is transmitted to the computer host through a common serial port,and the computer host is connected to the display through a common VGAcable; wherein the display is configured to display a virtual keyboardhaving a total of N keys on the display, the virtual keyboard isconfigured to flicker in rounds, and all N keys are configured toflicker randomly once in each round; wherein the electro-oculogramacquisition module is configured to acquire electro-oculogram signals ofa user in real-time through the electrode when the user blinks after theflickering of a target key when spelling, capture electro-oculogram datawithin a time period following the flickering of a key from the acquiredelectro-oculogram signals, use said electro-oculogram data as anoriginal feature vector of the flickering key, and generate N originalfeature vectors in each cycle; and wherein the computer host isconfigured to identify the N acquired original feature vectors andoutput a certain result from 0 to N through an electro-oculogramidentification algorithm, wherein 0 indicates that a condition forcharacter input is not met, and 1 to N correspond to the N keys on thevirtual keyboard.
 9. The input apparatus according to claim 8,characterized in that: the electro-oculogram acquisition modulecomprises a signal amplification module, a signal filtering module, anA/D conversion module, and a serial communication module, wherein: (1)the signal amplification module is based on an INA128 chip, having acommon mode rejection ratio of 120 db or more and an amplificationfactor of 1000 times or more; (2) the signal filtering module is basedon an OPA4227 chip, comprising a second-order Butterworth high-passfilter with a cutoff frequency of 3 Hz, a fourth-order Butterworthlow-pass filter with a cutoff frequency of 25 Hz, and a Butterworth 50Hz trap filter; (3) the A/D conversion module is based on an AD7606chip, having a conversion accuracy of 16 bits; and (4) the serialcommunication module adopts a serial communication module that comeswith STM32.
 10. The input apparatus according to claim 8, characterizedin that: the electrode is a common silver chloride electrode.